Determining on-line influence of a commentor, an individual, an entity, or a web-site, also to be referred herein as site, in social media becomes an increasingly important subject nowadays. A major problem facing marketers and public relation (PR) professionals revolves around the prolific use of social media sites and the awesome scale they have achieved. Literally, hundreds of thousands of videos, blog posts, podcasts, events, and social network interactions, such as wall posts, group postings, and others, occur daily. Due to the sheer volume of content, constantly changing landscape of popular sites, and hundreds of millions of users involved, it is impossible to determine who should be listened to and those who must be engaged.
Existing systems for determining influence in social media are site based, i.e. their models of influence are calculated on a per-site basis. If there is a one-to-one relationship between a site and a person (an author), then the influence is extrapolated to indicate the influence of the person for the medium, in which the site exists. For instance, if siteA is a blog with only one author, and all blogs are counted similarly, then the influence for siteA, as calculated by the prior art methods, would also indicate the influence for the author of the blog.
To the best of the knowledge of inventors, prior art methods predominantly calculate influence in social media by recursively analyzing inbound web page link counts. For example, siteA would have a higher influence score than siteB if the following approximate rules apply:
RULE 1: If the number of sites having links pointing at siteA is higher than the number of sites having links pointing at siteB; and
RULE 2: If the count of sites pointing at the sites that point at siteA is higher than the count of sites that are pointing at the sites that point at siteB.
Rule 2 is applied recursively.
Various issues exist with this prior art method, namely:
                The method assumes that the total influence of the sites can be measured by a single property and that no other factors affect influence to a scale large enough to invalidate using only inbound link count as the measured property;        The method assumes that the link graph representing all the links between the sites is complete enough to form a basis for determining an influence;        The method assumes that a link implies that the linker has been influenced by the site he is linking to, which is not necessarily the case;        The method does not account for connections someone may have with a site, if there is no link to track that connection, i.e. if a visitor does not own a blog, and therefore does not link out to anyone, but he is still a frequent visitor to the blog, e.g., http://www.autoblog.com, then the influence that Autoblog has over the visitor is not calculated; and        The method does not map properly to other types of content and methods of social media expression, e.g., link-analysis methods deployed to the blogosphere are not relevant in the micromedia sphere of Twitter, i.e. link analysis techniques do not translate to all forms of social media and therefore they leave out entire pools of influencers that use other media channels as their voice.        
Accordingly, there is a need in the industry for developing alternative and improved methods and system for determining on-line influence of individuals and commentors cited or published in social media content as well as for determining the influence of web-sites hosting the content.